Data Visualization in R with ggplot2

Ethan Fosse
October 11, 2016

Research Associate, Department of Sociology

Workshop Preliminaries

  1. Workshop Requirements
  2. Installing and loading ggplot2
  3. Our Research Question
  4. Roadmap for the Workshop

1. Workshop Requirements

Before You Begin

  1. You have access to a laptop computer and Internet service
  2. You have downloaded and installed R with RStudio
  3. You have downloaded the R Workshop files:
    • States.RData
    • ggplot2workshop.html

Go to: https://compass-workshops.github.io/info/

2. Installing and Loading ggplot2

The Power of R and R Packages

  • Since R is free and open-source, lots of people are writing R packages
  • An R package is just a collection of R functions, data, and code in a well-defined format
  • R packages can do everything from text mining (tm package) to data visualzation (ggplot2 package) to data wrangling (dplyr package)
  • Over 9,000 R packages as of 2016 on CRAN (Comprehensive R Archive Network)

Installing Packages in RStudio

  • To install R packages in RStudio: GUI versus R Console
  • 1. Using the GUI: Go to the Packages tab and click Install
  • 2. Using the R Console: install.packages("package_name")
    • package_name is just the name of the R package in quotes
    • Try this R Code: install.packages("ggplot2")

Loading an R Package For Use

  • Once you've installed an R package, it's then bundled with R and RStudio
  • You now have access to all of the functions, data, code, and other files associated with the installed R package
  • However, to access these files you must load your R package
    • Try this R Code: library(ggplot2)
  • You only have to install an R package once, but you must load it every time you start R

3. Our Research Question

The 2008 Presidential Election

Obama and McCain

The Electoral College Map

Obama and McCain Map

Examining the Election of 2008

  • Throughout this workshop we will consider the following question:
    • Why did some states go for Obama and others for McCain?
  • We'll explore this question using real data on 50 states
  • In the process of answering this question we'll learn about visualizing data with ggplot2

4. Roadmap for the Workshop

What We'll Learn Today

  • Part 1: Histograms and Density Plots
  • Part 2: Bar Plots and Faceting
  • Part 3: Boxplots
  • Part 4: Scatterplots
  • Part 5: Spatial Mapping

Part 1: Histograms and Density Plots

Mission #1: Educational Differences in the United States

  • How does the percent who went to high school differ across regions?

  • Later we'll examine if education might help us understand variation in voting across the United States

  • We'll use data to answer this question!

Loading Data into R

  • Let's load States.RData into our workspace

  • Using RStudio's user-friendly interface:

    1. File —> Open File
    2. Navigate to the location where you downloaded the files for this workshop (for example: C:/Folder/)

  • You can also try this R Code:

    load(C:/Folder/States.RData)

Viewing the Data as a Spreadsheet

  • Let's look at our data in more depth!
  • Let's use the function View()
  • Try this R Code:
View(States)
  • Ask yourself:
    • What are the observational units of the data set?
    • What variables look interesting or unusual to you?

Introducing the Grammar of Graphics

  • ggplot2 stands for Grammar of Graphics
  • The idea behind ggplot2 is that it breaks down plotting into a set of distinct inputs
  • There are two main functions in ggplot2: qplot() and ggplot()
  • We will focus more on qplot(), which stands for “quick plot”
  • qplot() is very powerful and has a similar syntax to the base R functions

Remember: Functions in R

  • The command qplot() is a function
  • We will use functions a lot!
  • A function takes one or more inputs (raw meat), does something to these inputs (grinds it up), and gives an output (ground meat)

Meat Grinder

The Basics of the qplot Function

  • The qplot() function has the following syntax:
qplot(x, y, data, color, shape, size, facets, geom, stat)
  • x and y: the variables to plot
  • data: your data set
  • color,shape, and size: aesthetic arguments
  • facets: optional splitting (or “faceting”) into subplots
  • geom: the actual visualization of the data (such as point or line)
  • stat: any statistical summaries to be applied

Creating a Histogram: State Populations

  • Try this R Code:
qplot(data=States, x=Population, geom="histogram", bins=10)
  • Ask yourself:
    • How would you describe the distribution of income across states?
    • What are the inputs into this function?
    • What happens if you change the number of bins = 10 to bins=20 or bins=5?

Creating a Density Plot: State Populations

  • Try this R Code:
qplot(data=States, x=Population, geom="density")
  • Ask yourself:
    • How would you describe the distribution of income across states?
    • What are the pros/cons of a density plot over a histogram?

Changing the Color

  • We use the I() function to specify a particular color
  • The I() function tells ggplot2 that this is a color, not an R object in its own right
  • Try this R Code:
qplot(data=States, x=Population, geom="histogram", color=I("red"), bins=20)

qplot(data=States, x=Population, geom="density", color=I("red"))
  • Ask yourself:
    • What is the “color” option doing to our graphs?
    • How do you change the color to “orange”?

Changing the Fill

  • Try this R Code:
qplot(data=States, x=Population, geom="histogram", fill=I("red"), bins=20)

qplot(data=States, x=Population, geom="density", fill=I("red"))
  • Ask yourself:
    • What is the “fill” option doing to our graphs?
    • How do you change the fill to “orange”?

Population by McCain/Obama Outcome: Color

  • We can also specify the color in terms of another variable (typically categorical)
  • Try this R Code:
qplot(data=States, x=Population, geom="histogram", color=ObamaMcCain, bins=20)

qplot(data=States, x=Population, geom="density", color=ObamaMcCain)
  • Ask yourself:
    • Were states higher in population more likely to go for Obama or McCain?

Population by McCain/Obama Outcome: Fill

  • Try this R Code:
qplot(data=States, x=Population, geom="histogram", fill=ObamaMcCain, bins=20)

qplot(data=States, x=Population, geom="density", fill=ObamaMcCain)
  • Ask yourself:
    • What is a problem with the “fill” option, particularly for the density plot?

Adding Transparency to the Plots

  • Try this R Code:
qplot(data=States, x=Population, geom="histogram", fill=ObamaMcCain, bins=20, alpha=I(0.5))

qplot(data=States, x=Population, geom="density", fill=ObamaMcCain, alpha=I(0.5))
  • Ask yourself:
    • What happens if you change alpha to 0.2? How about 0.8?
    • What happens if you change alpha to 1? How about 0?

Challenge #1: Educational Differences in the United States

  • How does the percent who went to high school differ across regions?
  • Create a density plot with ggplot2 to answer this question!
  • R Code Hint:
qplot(data=States, x=HighSchool, geom="density", fill=Region, alpha=I(0.5))

Check-In #1: Density Plots and Histograms

  • At this point you should have:
    • Loaded a data set into R
    • Created histograms and density plots
    • Explored the distribution of a continuous variable by levels of a categorical variable using the “fill” and “color” options
    • Altered the transparency of graphs using the “alpha” option

Part 2: Bar Plots and Faceting

Mission #2: Rich State, Poor State, Red State, Blue State

  • What are the number of “rich” states that went for Obama compared to “poor”“ states?
  • To answer this question, we'll create bar plots

Creating a Categorical Variable

  • We will use the recode command from the car package
  • You need to install the car R package and then load it
  • Try this R Code:
install.packages("car")
library("car")

States$Rich <- car::recode(States$HouseholdIncome, "lo:55000='Poor'; 55001:hi = 'Rich'", as.factor.result=TRUE)

table(States$Rich)
  • Ask yourself:
    • What are the advantages and disadvantages of converting a continuous variable to a categorical variable?

Creating a Bar Plot with a Single Categorical Variable

  • Try this R Code:
qplot(data=States, x=Rich, geom="bar")
qplot(data=States, x=Rich, color=Rich, geom="bar")
qplot(data=States, x=Rich, fill=Rich, geom="bar")
  • Ask yourself:
    • What is the difference between these plots?

Creating a Bar Plot with Two Categorical Variables

  • Try this R Code:
qplot(data=States, x=Rich, color=Region, geom="bar")

qplot(data=States, x=Rich, fill=Region, geom="bar")
  • Ask yourself:
    • What is the difference between these plots?
    • Which graph do you prefer?

Introduction to Faceting

  • Faceting entails creating sub-plots using values of one or more other variables
  • These are sometimes called a trellis graphs, since there are many sub-plots that give the appearance of a trellis
  • The syntax is row_variable ~ column_variable
  • To create a trellis graph based on a single conditioning variable, use row_variable ~ . or . ~ column_variable
  • The . indicates that we're not conditioning on the rows or columns, respectively

Faceting with Bar Plots

  • Try this R Code:
qplot(data = States, x = Region, fill = Region, geom = "bar", facets = Rich ~ .)

qplot(data = States, x = Region, fill = Region, geom = "bar", facets = . ~ Rich)

qplot(data = States, x =Region, fill = Region, geom = "bar", facets = ObamaMcCain ~ Rich)
  • Ask yourself:
    • How many states are “rich”, in the Northeast, and went for McCain?
    • How many states are “rich”, in the Northeast, and went for Obama?

Improving Plots with Axis Labels

  • Try this R Code:
qplot(data=States, x=Rich, fill=Region, geom="bar", xlab="Income Category", ylab="Number of States")

  • We can add axis labels to any qplot, not just bar plots

  • Try this R Code:

qplot(data=States, x=Population, geom="histogram", bins=10, xlab="Population (in Millions)", ylab="Number of States")

Improving Plots with a Main Title

  • Try this R Code:
qplot(data=States, x=Rich, fill=Region, geom="bar", main="Bar Plot of Income Category by Region")

  • We can add axis labels to any qplot, not just bar plots

  • Try this R Code:

qplot(data=States, x=Population, geom="histogram", bins=10, main="Histogram of Population")

Challenge #2: Rich State, Poor State, Red State, Blue State

  • What are the number of “rich” states that went for Obama compared to “poor” states?

  • Create a bar plot with ggplot2 to answer this question

  • R Code Hint:

qplot(data = States, x = Rich, fill = Rich, geom = "bar", facets = . ~ ObamaMcCain)
  • Is this finding surprising to you?
  • Be careful with interpreting aggregated data!

Check-In #2: Bar Plots and Faceting

  • At this point you should have:
    • Created a bar plot for a single categorical variable
    • Created bar plots using multiple categorical variables
    • Learned how to improve the axis labels and main title of a graph

Part 3: Boxplots

Mission #3: A Gap Between Red and Blue States?

  • A lot of researchers and pundits claim that there are fundamental differences between “red” and “blue” states
  • We will look at a number of differences
  • Between those states that went for McCain versus Obama, is there a difference in the percentage who went to college?

Creating Box Plots

  • These are also known as box-and-whisker plots

  • They're great for examining the distribution of a continuous variable by levels of a categorical variable

  • Try this R Code:

qplot(data=States, x=ObamaMcCain, y=HouseholdIncome, geom="boxplot")

qplot(data=States, x=ObamaMcCain, y=GSP, geom="boxplot")

qplot(data=States, x=ObamaMcCain, y=GSP, fill=ObamaMcCain, geom="boxplot")

Adding Points to Boxplots

  • We can also overlay the box plots wiht points using the c() function

  • Try this R Code:

qplot(data=States, x=ObamaMcCain, y=HouseholdIncome, geom=c("boxplot","point"))

qplot(data=States, x=ObamaMcCain, y=HouseholdIncome, geom=c("boxplot","jitter"))
  • Ask yourself:
    • What is the difference between using point versus jitter?

Labeling Points

  • We can also label points using the label option and by specifying text as a geom object to be plotted
  • The size option controls the size of the text labels
  • Try this R Code:
qplot(data=States, x=ObamaMcCain, y=HouseholdIncome, geom="boxplot")

qplot(data=States, x=ObamaMcCain, label=State, y=GSP, geom=c("boxplot","text"))
  • Ask yourself:
    • Which states are outliers?

Transforming a Variable

  • We can also transform a variable before plotting

  • Often skewed distributions are logged

  • Try this R Code:

qplot(data=States, x=ObamaMcCain, y=Population, geom="boxplot")

qplot(data=States, x=ObamaMcCain, y=Population, log="y", geom="boxplot")

Challenge #3: A Gap Between Red and Blue States?

  • Between those states that went for McCain versus Obama, is there a difference in the percentage that went to college?

  • R Code Hint:

qplot(data=States, x=ObamaMcCain, y=College, geom=c("boxplot","point"))

qplot(data=States, x=ObamaMcCain, y=College, label=State, geom=c("boxplot","text"))
  • Which states have the lowest and highest levels of education for each level of the variable ObamaMcCain?

Check-In #3: Box Plots

  • At this point you should have:
    • Generated box plots for a continuous variable across levels of a categorical variable
    • Placed points over a plot
    • Placed labels over a plot

Part 4: Scatter Plots

Mission #4: Explaining State-Level Voting

  • Our data set has several numerical variables, including HousedholdIncome, College, and NonWhite
  • Which of these variables do you think is most closely related to the percentage voting for Obama?

Scatter Plot of Two Numerical Variables

  • We can also transform a variable before plotting

  • Often skewed distributions are logged

  • Try this R Code:

qplot(data=States, x=College, y=HouseholdIncome, geom="point")
cor(States$College, States$HouseholdIncome)

qplot(data=States, x=College, y=NonWhite, geom="point")
cor(States$College, States$NonWhite)
  • Ask yourself:
    • In what way does a scatter plot reveal more information than a correlation?

Adding a Line

  • We can add a smoothed line over the graph, which includes standard errors to represent sampling uncertainty

  • Try this R Code:

qplot(data=States, x=College, y=HouseholdIncome, geom=c("point","smooth"))

qplot(data=States, x=College, y=NonWhite, geom=c("point","smooth"))
  • Ask yourself:
    • Are the relationships between these variables roughly linear?

Labeling Points

  • We can also replace the points with text labels

  • Try this R Code:

qplot(data=States, x=College, y=HouseholdIncome, label=State, geom=c("smooth", "text"))

qplot(data=States, x=College, y=NonWhite, label=State, geom=c("smooth", "text"))
  • Ask yourself:
    • Which states are outliers?
    • Are there any unusual data points?

Challenge #4: Explaining State-Level Voting

  • Our data set has several numerical variables, including HousedholdIncome, College, and NonWhite

  • Which of these variables do you think is most predictive of the percentage voting for Obama?

  • R Code Hint:

qplot(data=States, x=HouseholdIncome, y=ObamaVote, label=State, geom=c("text","smooth"))

qplot(data=States, x=College, y=ObamaVote, label=State, geom=c("text","smooth"))

qplot(data=States, x=NonWhite, y=ObamaVote, label=State, geom=c("text","smooth"))

Check-In #4: Scatter Plots

  • At this point you should have:
    • Created a scatter plot between two numerical variables
    • Generated a smoothing line over a scatter plot
    • Overlaid a scatter plot with text labels

5. Spatial Mapping

Mission #5: Which States are Red, Which States are Blue?

  • We will answer the question: which states are “red” and which states are “blue”?
  • We can answer this question by looking at the raw data, but a visualization will be much easier to interpret
  • Your goal is to create a map of the United States with information on the voting patterns

Grabbing Info on Latitude and Longitude

  • The ggplot2 function map_data() on latitude and longitude of U.S. states

  • Try this R Code:

USAMap <- map_data("state")
str(USAMap)
View(USAMap)
  • Ask yourself:
    • What kind of information is in this data set?
    • How might this data set help us create a map?

Merging the Two Data Sets

  • Our goal is to merge these two data sets, which requires a common variable

  • Try this R Code:

States$region <- tolower(States$State)

StatesMerged <- merge(x=USAMap, y=States, by = "region")

str(StatesMerged)
  • Ask yourself:
    • How many rows and columns are in StatesMerged?

Preparing for Mapmaking

  • The new data set StatesMerged also has information on latitude, longitude, and a variable called order
  • This encodes information on how to draw the state boundaries
  • We want the latitude and longitude values to be correctly sorted, so we will use the order() function
  • This just sorts the rows into ascending order so that the map will be drawn correctly

Preparing for Mapmaking (cont.)

  • Try this R Code:
StatesMerged <- StatesMerged[order(StatesMerged$order), ]
  • Ask yourself:
    • Are we sorting the rows or columns?
    • What is the name of the data set and the variable that is being sorted?

Map of Household Income

  • Now we're ready to create a map!

  • Try this R Code:

qplot(data = StatesMerged, x=long, y=lat, group = group, fill = HouseholdIncome, geom = "polygon")
  • Ask yourself:
    • Which states have the highest income? How about the lowest?

More Maps with Differing Colors

  • Let's create some more maps!

  • We'll use scale_fill_gradient() to alter the color gradient

  • Try this R Code:

qplot(data = StatesMerged, x=long, y=lat, group = group, fill = College, geom = "polygon") + scale_fill_gradient(low="red", high="green")
  • Ask yourself:
    • Which states have the highest income? How about the lowest?

Challenge #5: Which States are Red, Which States are Blue?

  • Which states are “red” and which states are “blue”?

  • If you've followed along, then you should already have the data set ready!

  • R Code Hint:

qplot(data = StatesMerged, x=long, y=lat, group = group, fill = ObamaVote, geom = "polygon") + scale_fill_gradient(low="red", high="blue")
  • Bonus:
    • Wait, why are so many states purple or pink-ish?

Check-In #5: Spatial Mapping

  • At this point you should have:
    • Loaded a data set with spatial information
    • Merged this data set with another data set
    • Created a map of U.S. states
    • Altered the color scheme of the map

Recap of the Workshop

  • At this point you should have:
    • Created density plots and histograms
    • Generated bar plots and box plots
    • Created scatter plots with smoothing lines and labels
    • Mapped social science data

Feedback Survey:

For More Information:

URL: https://compass-workshops.github.io/info/

Email List: Send an email to listserv@lists.princeton.edu with “Subscribe COMPASSWORKSHOPS” in the body and all other lines blank, including the subject

  • Free, open-source statistical programming and data analysis workshops using R and RStudio
  • Open to everyone with a Princeton ID
  • No programming experience is necessary or expected
  • Attendees should bring a laptop computer to fully participate in the workshops

Fall 2016 Schedule

Date Topic
September 20 Introduction to R and RStudio
September 27 Data Wrangling in R
October 4 Base R Graphics
October 11 Data Visualization in R with ggplot2
October 18 Programming Loops in R
November 8 Probability and Simulations in R
November 15 Monte Carlo Simulations in R
November 29 Text Analysis in R
December 6 Hypothesis Testing in R
December 13 Regression Analysis in R

Connect with Us:

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Our Website

URL: https://compass-workshops.github.io/info/

Our Mailing List

Send an email to listserv@lists.princeton.edu with “Subscribe COMPASSWORKSHOPS” in the body and all other lines blank, *including the subject*.

People

  • Teaching Staff

    • Ethan Fosse (Research Associate, Department of Sociology)
    • Yunkyu Sohn (Research Associate, Department of Politics)

  • Faculty Sponsors